Conventional analysis of a cervical histology image, such a pap smear or a biopsy sample, is performed by an expert
pathologist manually. This involves inspecting the sample for cellular level abnormalities and determining the spread of
the abnormalities. Cancer is graded based on the spread of the abnormal cells. This is a tedious, subjective and time-consuming
process with considerable variations in diagnosis between the experts. This paper presents a computer aided
decision support system (CADSS) tool to help the pathologists in their examination of the cervical cancer biopsies. The
main aim of the proposed CADSS system is to identify abnormalities and quantify cancer grading in a systematic and
repeatable manner. The paper proposes three different methods which presents and compares the results using 475
images of cervical biopsies which include normal, three stages of pre cancer, and malignant cases.
This paper will explore various components of an effective CADSS; image acquisition, pre-processing, segmentation,
feature extraction, classification, grading and disease identification. Cervical histological images are captured using a
digital microscope. The images are captured in sufficient resolution to retain enough information for effective
classification. Histology images of cervical biopsies consist of three major sections; background, stroma and squamous
epithelium. Most diagnostic information are contained within the epithelium region. This paper will present two levels of
segmentations; global (macro) and local (micro). At the global level the squamous epithelium is separated from the
background and stroma. At the local or cellular level, the nuclei and cytoplasm are segmented for further analysis. Image
features that influence the pathologists’ decision during the analysis and classification of a cervical biopsy are the
nuclei’s shape and spread; the ratio of the areas of nuclei and cytoplasm as well as the texture and spread of the
abnormalities. Similar features are extracted towards the automated classification process. This paper will present
various feature extraction methods including colour, shape and texture using Gabor wavelet as well as various quantative
metrics. Generated features are used to classify cells or regions into normal and abnormal categories. Following the
classification process, the cancer is graded based on the spread of the abnormal cells. This paper will present the results
of the grading process with five stages of the cancer spectrum.
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